October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Category Rule Learning Transfers to Target Verification but Often Fails to Transfer to Search Guidance
Author Affiliations & Notes
  • Ashley Ercolino
    University of Central Florida
  • Clay Killingsworth
    University of Central Florida
  • Corey Bohil
    University of Central Florida
  • Mark Neider
    University of Central Florida
  • Joseph Schmidt
    University of Central Florida
  • Footnotes
    Acknowledgements  Research reported in this publication was supported by the National Eye Institute of the National Institutes of Health under Award Number R15EY029511. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Journal of Vision October 2020, Vol.20, 1157. doi:https://doi.org/10.1167/jov.20.11.1157
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      Ashley Ercolino, Clay Killingsworth, Corey Bohil, Mark Neider, Joseph Schmidt; Category Rule Learning Transfers to Target Verification but Often Fails to Transfer to Search Guidance. Journal of Vision 2020;20(11):1157. doi: https://doi.org/10.1167/jov.20.11.1157.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Models of categorical search suggest there is a cyclical process in which attention is guided to the most category-consistent peripheral stimulus, which is subsequently categorized. This process repeats until the target is found or search terminates (Yu, et al., 2016). We asked if the same classification rule is used to guide attention to peripheral stimuli, and to categorize it. Compared to foveal categorization, low-acuity categorization in peripheral vision would be akin to making the categories less discriminable. Categories learned via explicit rule (RB) are more easily generalizable to stimuli in new feature spaces than categories learned via implicit rule (II; Casale, et al., 2012). Accordingly, we hypothesized that categorization rule use would transfer to search guidance moreso in RB relative to II. Participants learned to categorize sinusoidal gratings using an RB or II rule and then completed a search task in which eye-movements were recorded. Decision-bound models identified the rule participants used in category learning, and to direct attention to and verify the target during categorical search. Categorization and search target verification generally resulted in participants using an optimal classification rule (an integration or independent decision rule) regardless of categorization condition. However, categorical guidance, defined as the percentage of target first fixations, showed that only 30% of II participants used the optimal classification strategy, as opposed to 60% of RB participants. This suggests a disassociation between the rule used to categorize foveally and the rule used to categorize peripherally, with a larger discrepancy in II relative to RB. When examining search performance, participants who used the optimal rule to guide search had smaller RTs and stronger guidance (both, p<.05), regardless of condition. This suggests that despite demonstrating robust search guidance and the ability to categorize stimuli foveally, the optimal classification rule often fails to transfer to search guidance.

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